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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="preprocessor">#ifndef CAFFE_UTIL_MATH_FUNCTIONS_H_</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="preprocessor">#define CAFFE_UTIL_MATH_FUNCTIONS_H_</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;</div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="preprocessor">#include &lt;stdint.h&gt;</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="preprocessor">#include &lt;cmath&gt;</span>  <span class="comment">// for std::fabs and std::signbit</span></div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;</div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="preprocessor">#include &quot;glog/logging.h&quot;</span></div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;</div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="preprocessor">#include &quot;caffe/common.hpp&quot;</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="preprocessor">#include &quot;caffe/util/device_alternate.hpp&quot;</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#include &quot;caffe/util/mkl_alternate.hpp&quot;</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;</div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespacecaffe.html">caffe</a> {</div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;</div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment">// Caffe gemm provides a simpler interface to the gemm functions, with the</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment">// limitation that the data has to be contiguous in memory.</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="keywordtype">void</span> caffe_cpu_gemm(<span class="keyword">const</span> CBLAS_TRANSPOSE TransA,</div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;    <span class="keyword">const</span> CBLAS_TRANSPOSE TransB, <span class="keyword">const</span> <span class="keywordtype">int</span> M, <span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> <span class="keywordtype">int</span> K,</div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;    <span class="keyword">const</span> Dtype alpha, <span class="keyword">const</span> Dtype* A, <span class="keyword">const</span> Dtype* B, <span class="keyword">const</span> Dtype beta,</div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;    Dtype* C);</div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;</div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="keywordtype">void</span> caffe_cpu_gemv(<span class="keyword">const</span> CBLAS_TRANSPOSE TransA, <span class="keyword">const</span> <span class="keywordtype">int</span> M, <span class="keyword">const</span> <span class="keywordtype">int</span> N,</div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;    <span class="keyword">const</span> Dtype alpha, <span class="keyword">const</span> Dtype* A, <span class="keyword">const</span> Dtype* x, <span class="keyword">const</span> Dtype beta,</div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;    Dtype* y);</div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;</div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;<span class="keywordtype">void</span> caffe_axpy(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype alpha, <span class="keyword">const</span> Dtype* X,</div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;    Dtype* Y);</div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;</div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="keywordtype">void</span> caffe_cpu_axpby(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype alpha, <span class="keyword">const</span> Dtype* X,</div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;    <span class="keyword">const</span> Dtype beta, Dtype* Y);</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;</div><div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="keywordtype">void</span> caffe_copy(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype *X, Dtype *Y);</div><div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;</div><div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;<span class="keywordtype">void</span> caffe_set(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype alpha, Dtype *X);</div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;</div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="keyword">inline</span> <span class="keywordtype">void</span> caffe_memset(<span class="keyword">const</span> <span class="keywordtype">size_t</span> N, <span class="keyword">const</span> <span class="keywordtype">int</span> alpha, <span class="keywordtype">void</span>* X) {</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;  memset(X, alpha, N);  <span class="comment">// NOLINT(caffe/alt_fn)</span></div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;}</div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;</div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="keywordtype">void</span> caffe_add_scalar(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype alpha, Dtype *X);</div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;</div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;<span class="keywordtype">void</span> caffe_scal(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype alpha, Dtype *X);</div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;<span class="keywordtype">void</span> caffe_sqr(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype* a, Dtype* y);</div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;<span class="keywordtype">void</span> caffe_sqrt(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype* a, Dtype* y);</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;<span class="keywordtype">void</span> caffe_add(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype* a, <span class="keyword">const</span> Dtype* b, Dtype* y);</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;<span class="keywordtype">void</span> caffe_sub(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype* a, <span class="keyword">const</span> Dtype* b, Dtype* y);</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;<span class="keywordtype">void</span> caffe_mul(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype* a, <span class="keyword">const</span> Dtype* b, Dtype* y);</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;<span class="keywordtype">void</span> caffe_div(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype* a, <span class="keyword">const</span> Dtype* b, Dtype* y);</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;<span class="keywordtype">void</span> caffe_powx(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* a, <span class="keyword">const</span> Dtype b, Dtype* y);</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> caffe_rng_rand();</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;Dtype caffe_nextafter(<span class="keyword">const</span> Dtype b);</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;<span class="keywordtype">void</span> caffe_rng_uniform(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype a, <span class="keyword">const</span> Dtype b, Dtype* r);</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;<span class="keywordtype">void</span> caffe_rng_gaussian(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype mu, <span class="keyword">const</span> Dtype sigma,</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;                        Dtype* r);</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;<span class="keywordtype">void</span> caffe_rng_bernoulli(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype p, <span class="keywordtype">int</span>* r);</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;<span class="keywordtype">void</span> caffe_rng_bernoulli(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype p, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>* r);</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;<span class="keywordtype">void</span> caffe_exp(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* a, Dtype* y);</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;<span class="keywordtype">void</span> caffe_log(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* a, Dtype* y);</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;<span class="keywordtype">void</span> caffe_abs(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* a, Dtype* y);</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;Dtype caffe_cpu_dot(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* x, <span class="keyword">const</span> Dtype* y);</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;Dtype caffe_cpu_strided_dot(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* x, <span class="keyword">const</span> <span class="keywordtype">int</span> incx,</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    <span class="keyword">const</span> Dtype* y, <span class="keyword">const</span> <span class="keywordtype">int</span> incy);</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;<span class="comment">// Returns the sum of the absolute values of the elements of vector x</span></div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;Dtype caffe_cpu_asum(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* x);</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;<span class="comment">// the branchless, type-safe version from</span></div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;<span class="comment">// http://stackoverflow.com/questions/1903954/is-there-a-standard-sign-function-signum-sgn-in-c-c</span></div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;<span class="keyword">inline</span> int8_t caffe_sign(Dtype val) {</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;  <span class="keywordflow">return</span> (Dtype(0) &lt; val) - (val &lt; Dtype(0));</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;}</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;<span class="comment">// The following two macros are modifications of DEFINE_VSL_UNARY_FUNC</span></div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;<span class="comment">//   in include/caffe/util/mkl_alternate.hpp authored by @Rowland Depp.</span></div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;<span class="comment">// Please refer to commit 7e8ef25c7 of the boost-eigen branch.</span></div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;<span class="comment">// Git cherry picking that commit caused a conflict hard to resolve and</span></div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;<span class="comment">//   copying that file in convenient for code reviewing.</span></div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;<span class="comment">// So they have to be pasted here temporarily.</span></div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;<span class="preprocessor">#define DEFINE_CAFFE_CPU_UNARY_FUNC(name, operation) \</span></div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;<span class="preprocessor">  template&lt;typename Dtype&gt; \</span></div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;<span class="preprocessor">  void caffe_cpu_##name(const int n, const Dtype* x, Dtype* y) { \</span></div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;<span class="preprocessor">    CHECK_GT(n, 0); CHECK(x); CHECK(y); \</span></div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;<span class="preprocessor">    for (int i = 0; i &lt; n; ++i) { \</span></div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;<span class="preprocessor">      operation; \</span></div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;<span class="preprocessor">    } \</span></div><div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;<span class="preprocessor">  }</span></div><div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;</div><div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;<span class="comment">// output is 1 for the positives, 0 for zero, and -1 for the negatives</span></div><div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;DEFINE_CAFFE_CPU_UNARY_FUNC(sign, y[i] = caffe_sign&lt;Dtype&gt;(x[i]))</div><div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;</div><div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;<span class="comment">// This returns a nonzero value if the input has its sign bit set.</span></div><div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;<span class="comment">// The name sngbit is meant to avoid conflicts with std::signbit in the macro.</span></div><div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;<span class="comment">// The extra parens are needed because CUDA &lt; 6.5 defines signbit as a macro,</span></div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;<span class="comment">// and we don&#39;t want that to expand here when CUDA headers are also included.</span></div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, \</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;    y[i] = static_cast&lt;bool&gt;((std::signbit)(x[i])))</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;DEFINE_CAFFE_CPU_UNARY_FUNC(fabs, y[i] = std::fabs(x[i]))</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;<span class="keywordtype">void</span> caffe_cpu_scale(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype alpha, <span class="keyword">const</span> Dtype *x, Dtype* y);</div><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;</div><div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;<span class="preprocessor">#ifndef CPU_ONLY  // GPU</span></div><div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;</div><div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;<span class="comment">// Decaf gpu gemm provides an interface that is almost the same as the cpu</span></div><div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;<span class="comment">// gemm function - following the c convention and calling the fortran-order</span></div><div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;<span class="comment">// gpu code under the hood.</span></div><div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;<span class="keywordtype">void</span> caffe_gpu_gemm(<span class="keyword">const</span> CBLAS_TRANSPOSE TransA,</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;    <span class="keyword">const</span> CBLAS_TRANSPOSE TransB, <span class="keyword">const</span> <span class="keywordtype">int</span> M, <span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> <span class="keywordtype">int</span> K,</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;    <span class="keyword">const</span> Dtype alpha, <span class="keyword">const</span> Dtype* A, <span class="keyword">const</span> Dtype* B, <span class="keyword">const</span> Dtype beta,</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;    Dtype* C);</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;</div><div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;<span class="keywordtype">void</span> caffe_gpu_gemv(<span class="keyword">const</span> CBLAS_TRANSPOSE TransA, <span class="keyword">const</span> <span class="keywordtype">int</span> M, <span class="keyword">const</span> <span class="keywordtype">int</span> N,</div><div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;    <span class="keyword">const</span> Dtype alpha, <span class="keyword">const</span> Dtype* A, <span class="keyword">const</span> Dtype* x, <span class="keyword">const</span> Dtype beta,</div><div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;    Dtype* y);</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;<span class="keywordtype">void</span> caffe_gpu_axpy(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype alpha, <span class="keyword">const</span> Dtype* X,</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;    Dtype* Y);</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;<span class="keywordtype">void</span> caffe_gpu_axpby(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype alpha, <span class="keyword">const</span> Dtype* X,</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;    <span class="keyword">const</span> Dtype beta, Dtype* Y);</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;<span class="keywordtype">void</span> caffe_gpu_memcpy(<span class="keyword">const</span> <span class="keywordtype">size_t</span> N, <span class="keyword">const</span> <span class="keywordtype">void</span> *X, <span class="keywordtype">void</span> *Y);</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;<span class="keywordtype">void</span> caffe_gpu_set(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype alpha, Dtype *X);</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;<span class="keyword">inline</span> <span class="keywordtype">void</span> caffe_gpu_memset(<span class="keyword">const</span> <span class="keywordtype">size_t</span> N, <span class="keyword">const</span> <span class="keywordtype">int</span> alpha, <span class="keywordtype">void</span>* X) {</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;<span class="preprocessor">#ifndef CPU_ONLY</span></div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;  CUDA_CHECK(cudaMemset(X, alpha, N));  <span class="comment">// NOLINT(caffe/alt_fn)</span></div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;<span class="preprocessor">#else</span></div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;  NO_GPU;</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;<span class="preprocessor">#endif</span></div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;}</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;<span class="keywordtype">void</span> caffe_gpu_add_scalar(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype alpha, Dtype *X);</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;<span class="keywordtype">void</span> caffe_gpu_scal(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype alpha, Dtype *X);</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;<span class="preprocessor">#ifndef CPU_ONLY</span></div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;<span class="keywordtype">void</span> caffe_gpu_scal(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype alpha, Dtype* X, cudaStream_t str);</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;<span class="preprocessor">#endif</span></div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;<span class="keywordtype">void</span> caffe_gpu_add(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype* a, <span class="keyword">const</span> Dtype* b, Dtype* y);</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;<span class="keywordtype">void</span> caffe_gpu_sub(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype* a, <span class="keyword">const</span> Dtype* b, Dtype* y);</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;<span class="keywordtype">void</span> caffe_gpu_mul(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype* a, <span class="keyword">const</span> Dtype* b, Dtype* y);</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;<span class="keywordtype">void</span> caffe_gpu_div(<span class="keyword">const</span> <span class="keywordtype">int</span> N, <span class="keyword">const</span> Dtype* a, <span class="keyword">const</span> Dtype* b, Dtype* y);</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;<span class="keywordtype">void</span> caffe_gpu_abs(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* a, Dtype* y);</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;<span class="keywordtype">void</span> caffe_gpu_exp(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* a, Dtype* y);</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;<span class="keywordtype">void</span> caffe_gpu_log(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* a, Dtype* y);</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;<span class="keywordtype">void</span> caffe_gpu_powx(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* a, <span class="keyword">const</span> Dtype b, Dtype* y);</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;<span class="keywordtype">void</span> caffe_gpu_sqrt(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* a, Dtype* y);</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;<span class="comment">// caffe_gpu_rng_uniform with two arguments generates integers in the range</span></div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;<span class="comment">// [0, UINT_MAX].</span></div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;<span class="keywordtype">void</span> caffe_gpu_rng_uniform(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>* r);</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;<span class="comment">// caffe_gpu_rng_uniform with four arguments generates floats in the range</span></div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;<span class="comment">// (a, b] (strictly greater than a, less than or equal to b) due to the</span></div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;<span class="comment">// specification of curandGenerateUniform.  With a = 0, b = 1, just calls</span></div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;<span class="comment">// curandGenerateUniform; with other limits will shift and scale the outputs</span></div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;<span class="comment">// appropriately after calling curandGenerateUniform.</span></div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;<span class="keywordtype">void</span> caffe_gpu_rng_uniform(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype a, <span class="keyword">const</span> Dtype b, Dtype* r);</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;<span class="keywordtype">void</span> caffe_gpu_rng_gaussian(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype mu, <span class="keyword">const</span> Dtype sigma,</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;                            Dtype* r);</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;<span class="keywordtype">void</span> caffe_gpu_rng_bernoulli(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype p, <span class="keywordtype">int</span>* r);</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;<span class="keywordtype">void</span> caffe_gpu_dot(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* x, <span class="keyword">const</span> Dtype* y, Dtype* out);</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;<span class="keywordtype">void</span> caffe_gpu_asum(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* x, Dtype* y);</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;</div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;<span class="keywordtype">void</span> caffe_gpu_sign(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* x, Dtype* y);</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;<span class="keywordtype">void</span> caffe_gpu_sgnbit(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* x, Dtype* y);</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;<span class="keywordtype">void</span> caffe_gpu_fabs(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype* x, Dtype* y);</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Dtype&gt;</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;<span class="keywordtype">void</span> caffe_gpu_scale(<span class="keyword">const</span> <span class="keywordtype">int</span> n, <span class="keyword">const</span> Dtype alpha, <span class="keyword">const</span> Dtype *x, Dtype* y);</div><div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;</div><div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;<span class="preprocessor">#define DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(name, operation) \</span></div><div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;<span class="preprocessor">template&lt;typename Dtype&gt; \</span></div><div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;<span class="preprocessor">__global__ void name##_kernel(const int n, const Dtype* x, Dtype* y) { \</span></div><div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;<span class="preprocessor">  CUDA_KERNEL_LOOP(index, n) { \</span></div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;<span class="preprocessor">    operation; \</span></div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;<span class="preprocessor">  } \</span></div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;<span class="preprocessor">} \</span></div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;<span class="preprocessor">template &lt;&gt; \</span></div><div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;<span class="preprocessor">void caffe_gpu_##name&lt;float&gt;(const int n, const float* x, float* y) { \</span></div><div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;<span class="preprocessor">  </span><span class="comment">/* NOLINT_NEXT_LINE(whitespace/operators) */</span><span class="preprocessor"> \</span></div><div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;<span class="preprocessor">  name##_kernel&lt;float&gt;&lt;&lt;&lt;CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS&gt;&gt;&gt;( \</span></div><div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;<span class="preprocessor">      n, x, y); \</span></div><div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;<span class="preprocessor">} \</span></div><div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;<span class="preprocessor">template &lt;&gt; \</span></div><div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;<span class="preprocessor">void caffe_gpu_##name&lt;double&gt;(const int n, const double* x, double* y) { \</span></div><div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;<span class="preprocessor">  </span><span class="comment">/* NOLINT_NEXT_LINE(whitespace/operators) */</span><span class="preprocessor"> \</span></div><div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;<span class="preprocessor">  name##_kernel&lt;double&gt;&lt;&lt;&lt;CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS&gt;&gt;&gt;( \</span></div><div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;<span class="preprocessor">      n, x, y); \</span></div><div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;<span class="preprocessor">}</span></div><div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;</div><div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;<span class="preprocessor">#endif  // !CPU_ONLY</span></div><div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;</div><div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;}  <span class="comment">// namespace caffe</span></div><div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;</div><div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;<span class="preprocessor">#endif  // CAFFE_UTIL_MATH_FUNCTIONS_H_</span></div><div class="ttc" id="namespacecaffe_html"><div class="ttname"><a href="namespacecaffe.html">caffe</a></div><div class="ttdoc">A layer factory that allows one to register layers. During runtime, registered layers can be called b...</div><div class="ttdef"><b>Definition:</b> blob.hpp:14</div></div>
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